Lorena Mesa

Lorena's time at Obama for America and her subsequent graduate research required her to learn how to transform messy, incomplete data into intelligible analysis on topics like predicting Latinx voter behavior.

It's this unique background in research and applied mathematics that drove Lorena to pursue a career in engineering and data science. One part activist, one part Star Wars fanatic, and another part Trekkie, Lorena abides by the motto to "live long and prosper".

Adam Tornhill

Adam Tornhill is a programmer who combines degrees in engineering and psychology.

He's the founder of Empear where he designs tools for software analysis. He's also the author of Software Design X-Rays, the best selling Your Code as a Crime Scene, Lisp for the Web, and Patterns in C. Adam's other interests include modern history, music, and martial arts.

Our schedule

8:30 Registration Opens

9:00 Opening Keynote

Lorena Mesa

We Pythonistas welcome newcomers with the wisdom of Tim Peter’s “import this”. Okay, well maybe. The Zen of Python provides us as a community general aphorisms on how to write Python and how to be a good Pythonista by offering loose guidelines that promotes discussion. What lessons, then, can the Zen of Python teach us about Data Ethics?

Data ethics is a nebulous concept, a necessity in the era of algorithms and the data economy. Together we’ll review some stories from the headlines about the data economy where there were ethical concerns and apply the Zen of Python. Starting with the impact of social media likes on political campaigns to censorship on social media in the #MeToo movement, we’ll use big challenges to highlight obvious and not so obvious lessons. We’ll wrap this discussion with how Python today is helping challenge data ethics concerns and what you can do to participate in this fight.

Ultimately the Zen of Python teaches us that ‘Now is Better than Never’ and we must ask as data practitioners - what principles will we develop and champion to respond to ethical dilemmas?

10:00

Diego Moreda Rodriguez

Quantum Computing is here, and can be explored from the comfort of your favorite Python IDE.
Join the quantum journey: from the basics of quantum mechanics to the development of Qiskit, along with the challenges faced
building an open source project from the scientific world into the developer world.

10:35 Break

10:45

Alexander Hultnér

Python 3.7 is here and the @dataclass-decorator is a major new feature simplifying class-creation. In this talk, we will learn to use the power of data classes to make our codebases cleaner and leaner in a pythonic way.

We will also learn how to use the backport in Python 3.6 codebases before upgrading.

11:20

Samuel Regandell

Text-based Multi-User Dungeons (MUDs) were the first MMOs. Not only are they still played, they are great for small teams learning Python and game development.
This talk introduces the open-source Evennia MUD engine for developing a new MUD in pure Python using Django and Twisted.

11:55

Sebastian Ånerud

When creating complex and pipelined jobs in PySpark, your code quickly gets unstructured and virtually impossible to read. Especially when working with the dataframe API. As part of a large scale project, we developed a flexible code structure, dependent on the Spark SQL interface, which allowed easy addition of new jobs, increased readability and made collaboration easier.

In this presentation we share our key findings in how to structure your project for readability, flexibility and maintainability.

12:30 Lunch

13:30 Lightning Talks

14:15

Binbin Su

Gait phase recognition is of great importance to develop accurate timing feedback for exoskeleton control.

A powered exoskeleton can provide proper assistance to the subject if the current gait phase can be identified accurately. Machine learning classifiers were built in python to distinguish 4 gait phases in a gait cycle.

15:05

Åke Forslund

15:35 Break

15:45

Ross Taylor

Mantra is a new open-source Python library for managing deep learning projects. In this talk, I demonstrate how to use Mantra for some example deep learning projects, and how it can help machine learning engineers quickly iterate and move from idea to production.

16:25

Pradip Caulagi

Want to handle a lot of traffic with the same hardware? This talk will show you asyncio examples. Together we will build a loop where you’ll be buying the latest accessories for your new horse! We will also explore this in other event loop such as Trio and Curio.

17:05 Closing Keynote

Adam Tornhill

Most organizations find it hard to prioritize and repay their technical debt. The main reason is due to the scale of modern systems with million lines of code and multiple development teams; No one has a holistic overview. So what if we could mine the collective intelligence of all contributing programmers and start to make decisions based on data from how the organization actually works with the code? This session introduces one such approach with the potential to change how we view software systems.

In this session you'll get an introduction to techniques that help us uncover both problematic code as well as the social dimension of the teams that build your software. The techniques are based on software evolution and findings from various fields within psychology. This combination lets you prioritize the parts of your system that benefit the most from improvements, detect organizational issues and make practical decisions guided by data. Each point is illustrated with a case study from a real-world codebase. This is a new perspective on software development that will change how you work with code.